{
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   "source": [
    "# 需要用到的包\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  ###防止无法显示中文并设置黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  ###用来正常显示负号"
   ]
  },
  {
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   "execution_count": 2,
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pre&#x27; (year: 56, lat: 29, lon: 65)&gt;\n",
       "array([[[ 565.7156 ,  495.15598,  471.00815, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 526.20874,  463.16556,  419.1885 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 485.5093 ,  422.86063,  360.48846, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 927.5284 ,  796.25165,  783.7989 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 921.5262 ,  820.11584,  798.51184, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 772.6701 ,  814.7526 ,  734.5804 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[ 565.54504,  540.2187 ,  566.9998 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 536.631  ,  525.22046,  493.80664, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 512.51996,  515.54517,  509.23154, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [ 700.82446,  677.18896,  663.2068 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 620.47705,  618.12463,  611.3699 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 510.42694,  533.86566,  535.6796 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[ 615.21045,  590.4197 ,  539.86475, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 605.4814 ,  559.6482 ,  506.38144, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 586.1322 ,  498.5821 ,  482.23874, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 457.47565,  419.9706 ,  426.3118 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 409.12506,  379.74026,  382.93893, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 327.25058,  312.23038,  321.9389 , ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 106.0 106.2 106.5 106.8 ... 121.2 121.5 121.8 122.0\n",
       "  * lat      (lat) float64 26.0 26.25 26.5 26.75 27.0 ... 32.25 32.5 32.75 33.0\n",
       "  * year     (year) int64 1961 1962 1963 1964 1965 ... 2012 2013 2014 2015 2016</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 56</li><li><span class='xr-has-index'>lat</span>: 29</li><li><span class='xr-has-index'>lon</span>: 65</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b34b56c2-1716-4f00-bc13-a9907c303aa4' class='xr-array-in' type='checkbox' checked><label for='section-b34b56c2-1716-4f00-bc13-a9907c303aa4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>565.7 495.2 471.0 453.8 421.7 427.0 ... 658.5 nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[ 565.7156 ,  495.15598,  471.00815, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 526.20874,  463.16556,  419.1885 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 485.5093 ,  422.86063,  360.48846, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 927.5284 ,  796.25165,  783.7989 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 921.5262 ,  820.11584,  798.51184, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 772.6701 ,  814.7526 ,  734.5804 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[ 565.54504,  540.2187 ,  566.9998 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 536.631  ,  525.22046,  493.80664, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 512.51996,  515.54517,  509.23154, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [ 700.82446,  677.18896,  663.2068 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 620.47705,  618.12463,  611.3699 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 510.42694,  533.86566,  535.6796 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[ 615.21045,  590.4197 ,  539.86475, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 605.4814 ,  559.6482 ,  506.38144, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 586.1322 ,  498.5821 ,  482.23874, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 457.47565,  419.9706 ,  426.3118 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 409.12506,  379.74026,  382.93893, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 327.25058,  312.23038,  321.9389 , ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-c7bf7c31-b021-41a3-8f64-3510dc253205' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7bf7c31-b021-41a3-8f64-3510dc253205' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>106.0 106.2 106.5 ... 121.8 122.0</div><input id='attrs-d28a7f45-c86e-4773-9482-455f40c54448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d28a7f45-c86e-4773-9482-455f40c54448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfe8b88f-ccad-4c68-b84a-c29dc6efa219' class='xr-var-data-in' type='checkbox'><label for='data-bfe8b88f-ccad-4c68-b84a-c29dc6efa219' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([106.  , 106.25, 106.5 , 106.75, 107.  , 107.25, 107.5 , 107.75, 108.  ,\n",
       "       108.25, 108.5 , 108.75, 109.  , 109.25, 109.5 , 109.75, 110.  , 110.25,\n",
       "       110.5 , 110.75, 111.  , 111.25, 111.5 , 111.75, 112.  , 112.25, 112.5 ,\n",
       "       112.75, 113.  , 113.25, 113.5 , 113.75, 114.  , 114.25, 114.5 , 114.75,\n",
       "       115.  , 115.25, 115.5 , 115.75, 116.  , 116.25, 116.5 , 116.75, 117.  ,\n",
       "       117.25, 117.5 , 117.75, 118.  , 118.25, 118.5 , 118.75, 119.  , 119.25,\n",
       "       119.5 , 119.75, 120.  , 120.25, 120.5 , 120.75, 121.  , 121.25, 121.5 ,\n",
       "       121.75, 122.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.0 26.25 26.5 ... 32.5 32.75 33.0</div><input id='attrs-fbae8752-839d-41cf-ab68-783961a47f18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbae8752-839d-41cf-ab68-783961a47f18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6f855ed-7bac-4a3c-bba8-bdb9cd434ce9' class='xr-var-data-in' type='checkbox'><label for='data-b6f855ed-7bac-4a3c-bba8-bdb9cd434ce9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([26.  , 26.25, 26.5 , 26.75, 27.  , 27.25, 27.5 , 27.75, 28.  , 28.25,\n",
       "       28.5 , 28.75, 29.  , 29.25, 29.5 , 29.75, 30.  , 30.25, 30.5 , 30.75,\n",
       "       31.  , 31.25, 31.5 , 31.75, 32.  , 32.25, 32.5 , 32.75, 33.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1961 1962 1963 ... 2014 2015 2016</div><input id='attrs-5a33c245-ff88-4440-859d-e1a0c1347210' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a33c245-ff88-4440-859d-e1a0c1347210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06d49aa9-9ef3-4192-8c0f-55be86ef5155' class='xr-var-data-in' type='checkbox'><label for='data-06d49aa9-9ef3-4192-8c0f-55be86ef5155' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,\n",
       "       1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984,\n",
       "       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
       "       2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56c6294d-85d0-42fe-8e80-9994ad3d6f15' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-56c6294d-85d0-42fe-8e80-9994ad3d6f15' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'pre' (year: 56, lat: 29, lon: 65)>\n",
       "array([[[ 565.7156 ,  495.15598,  471.00815, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 526.20874,  463.16556,  419.1885 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 485.5093 ,  422.86063,  360.48846, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 927.5284 ,  796.25165,  783.7989 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 921.5262 ,  820.11584,  798.51184, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 772.6701 ,  814.7526 ,  734.5804 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[ 565.54504,  540.2187 ,  566.9998 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 536.631  ,  525.22046,  493.80664, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 512.51996,  515.54517,  509.23154, ...,        nan,\n",
       "                nan,        nan],\n",
       "...\n",
       "        [ 700.82446,  677.18896,  663.2068 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 620.47705,  618.12463,  611.3699 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 510.42694,  533.86566,  535.6796 , ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[ 615.21045,  590.4197 ,  539.86475, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 605.4814 ,  559.6482 ,  506.38144, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 586.1322 ,  498.5821 ,  482.23874, ...,        nan,\n",
       "                nan,        nan],\n",
       "        ...,\n",
       "        [ 457.47565,  419.9706 ,  426.3118 , ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 409.12506,  379.74026,  382.93893, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [ 327.25058,  312.23038,  321.9389 , ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 106.0 106.2 106.5 106.8 ... 121.2 121.5 121.8 122.0\n",
       "  * lat      (lat) float64 26.0 26.25 26.5 26.75 27.0 ... 32.25 32.5 32.75 33.0\n",
       "  * year     (year) int64 1961 1962 1963 1964 1965 ... 2012 2013 2014 2015 2016"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "rain = xr.open_dataset('data\\\\CN05.1_Pre_1961_2017_month_025x025.nc')['pre']\n",
    "rain = rain.loc[rain.time.dt.month.isin([6, 7, 8])].loc[:, 26:33, 106:122]\n",
    "rain = rain.groupby(rain.time.dt.year).mean(dim='time')\n",
    "rain = rain * 92\n",
    "rain"
   ]
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       ".xr-attrs dt {\n",
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       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;hgt&#x27; (time: 168, lat: 73, lon: 144)&gt;\n",
       "[1766016 values with dtype=float32]\n",
       "Coordinates:\n",
       "    level    float32 500.0\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "  * time     (time) datetime64[ns] 1960-12-01 1961-01-01 ... 2016-02-01\n",
       "Attributes:\n",
       "    long_name:     Monthly mean geopotential height\n",
       "    valid_range:   [ -700. 35000.]\n",
       "    units:         m\n",
       "    precision:     0\n",
       "    GRIB_id:       7\n",
       "    GRIB_name:     HGT\n",
       "    var_desc:      Geopotential height\n",
       "    level_desc:    Multiple levels\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Other\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [ -354.45834 32321.098  ]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'hgt'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 168</li><li><span class='xr-has-index'>lat</span>: 73</li><li><span class='xr-has-index'>lon</span>: 144</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e40d07a3-5f5b-4666-952a-b8b9c70a6601' class='xr-array-in' type='checkbox' checked><label for='section-e40d07a3-5f5b-4666-952a-b8b9c70a6601' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1766016 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-19fb2800-0c07-4217-bbaa-ebb451aee9be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-19fb2800-0c07-4217-bbaa-ebb451aee9be' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>level</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0</div><input id='attrs-98e63ce0-c2bb-4d7c-9b49-ee85569d6139' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98e63ce0-c2bb-4d7c-9b49-ee85569d6139' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cda4201a-71bc-4725-9e8c-14a02832f748' class='xr-var-data-in' type='checkbox'><label for='data-cda4201a-71bc-4725-9e8c-14a02832f748' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>millibar</dd><dt><span>long_name :</span></dt><dd>Level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>GRIB_id :</span></dt><dd>100</dd><dt><span>GRIB_name :</span></dt><dd>hPa</dd><dt><span>actual_range :</span></dt><dd>[1000.   10.]</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(500., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 87.5 85.0 ... -87.5 -90.0</div><input id='attrs-51839676-d848-40ed-a7ed-69f7cf676096' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51839676-d848-40ed-a7ed-69f7cf676096' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-529b7f03-f974-4e78-a01b-767e43e875ef' class='xr-var-data-in' type='checkbox'><label for='data-529b7f03-f974-4e78-a01b-767e43e875ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,\n",
       "        65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,\n",
       "        40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,\n",
       "        15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,\n",
       "       -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,\n",
       "       -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,\n",
       "       -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,\n",
       "       -85. , -87.5, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-8b437ae0-cb0a-4787-b7c1-64940c04707d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b437ae0-cb0a-4787-b7c1-64940c04707d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bfe5b56-52f1-4fc2-9476-9d017796d2d2' class='xr-var-data-in' type='checkbox'><label for='data-3bfe5b56-52f1-4fc2-9476-9d017796d2d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-12-01 ... 2016-02-01</div><input id='attrs-f5893250-ca77-4f43-9888-8c42195793e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5893250-ca77-4f43-9888-8c42195793e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef98abd0-fec5-41d0-a422-722141fa5029' class='xr-var-data-in' type='checkbox'><label for='data-ef98abd0-fec5-41d0-a422-722141fa5029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[1297320. 1921128.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-12-01T00:00:00.000000000&#x27;, &#x27;1961-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-02-01T00:00:00.000000000&#x27;, &#x27;1961-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-01-01T00:00:00.000000000&#x27;, &#x27;1962-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-12-01T00:00:00.000000000&#x27;, &#x27;1963-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1963-02-01T00:00:00.000000000&#x27;, &#x27;1963-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-01-01T00:00:00.000000000&#x27;, &#x27;1964-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-12-01T00:00:00.000000000&#x27;, &#x27;1965-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1965-02-01T00:00:00.000000000&#x27;, &#x27;1965-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-01-01T00:00:00.000000000&#x27;, &#x27;1966-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-12-01T00:00:00.000000000&#x27;, &#x27;1967-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1967-02-01T00:00:00.000000000&#x27;, &#x27;1967-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-01-01T00:00:00.000000000&#x27;, &#x27;1968-02-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1970-01-01T00:00:00.000000000&#x27;, &#x27;1970-02-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1972-12-01T00:00:00.000000000&#x27;, &#x27;1973-01-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1974-12-01T00:00:00.000000000&#x27;, &#x27;1975-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1975-02-01T00:00:00.000000000&#x27;, &#x27;1975-12-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1977-02-01T00:00:00.000000000&#x27;, &#x27;1977-12-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1979-02-01T00:00:00.000000000&#x27;, &#x27;1979-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1987-02-01T00:00:00.000000000&#x27;, &#x27;1987-12-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1992-02-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1996-01-01T00:00:00.000000000&#x27;, &#x27;1996-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-12-01T00:00:00.000000000&#x27;, &#x27;1997-01-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1998-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-12-01T00:00:00.000000000&#x27;, &#x27;1999-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-02-01T00:00:00.000000000&#x27;, &#x27;1999-12-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2006-12-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2008-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-12-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-02-01T00:00:00.000000000&#x27;, &#x27;2009-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2014-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-02-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-083ab325-f186-48d2-a3ce-aaaf78abd81c' class='xr-section-summary-in' type='checkbox'  ><label for='section-083ab325-f186-48d2-a3ce-aaaf78abd81c' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly mean geopotential height</dd><dt><span>valid_range :</span></dt><dd>[ -700. 35000.]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>GRIB_id :</span></dt><dd>7</dd><dt><span>GRIB_name :</span></dt><dd>HGT</dd><dt><span>var_desc :</span></dt><dd>Geopotential height</dd><dt><span>level_desc :</span></dt><dd>Multiple levels</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis Derived Products</dd><dt><span>actual_range :</span></dt><dd>[ -354.45834 32321.098  ]</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'hgt' (time: 168, lat: 73, lon: 144)>\n",
       "[1766016 values with dtype=float32]\n",
       "Coordinates:\n",
       "    level    float32 500.0\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "  * time     (time) datetime64[ns] 1960-12-01 1961-01-01 ... 2016-02-01\n",
       "Attributes:\n",
       "    long_name:     Monthly mean geopotential height\n",
       "    valid_range:   [ -700. 35000.]\n",
       "    units:         m\n",
       "    precision:     0\n",
       "    GRIB_id:       7\n",
       "    GRIB_name:     HGT\n",
       "    var_desc:      Geopotential height\n",
       "    level_desc:    Multiple levels\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Other\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [ -354.45834 32321.098  ]"
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     },
     "execution_count": 3,
     "metadata": {},
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   "source": [
    "hgt = xr.open_dataset('data\\\\hgt.mon.mean.nc')['hgt'].loc[:, 500, :, :]\n",
    "hgt = hgt.loc[hgt.time.dt.month.isin([12, 1, 2])].loc['1960-03-01':'2016-03-01':, :, :]\n",
    "hgt"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 168, lat: 89, lon: 180)&gt;\n",
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       "    valid_range:   [-5. 40.]\n",
       "    dataset:       NOAA Extended Reconstructed SST V4\n",
       "    parent_stat:   Individual Values</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 168</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-26a62d76-c988-46ad-aaaa-55fa98c11e29' class='xr-array-in' type='checkbox' checked><label for='section-26a62d76-c988-46ad-aaaa-55fa98c11e29' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2691360 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-0212d4c7-1e0d-4c73-bf8b-ceae826b996a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0212d4c7-1e0d-4c73-bf8b-ceae826b996a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-8fa84be0-dd4f-46aa-87a7-b421bec52246' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fa84be0-dd4f-46aa-87a7-b421bec52246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7f8ddff-1c23-4532-b20b-e55419a59985' class='xr-var-data-in' type='checkbox'><label for='data-f7f8ddff-1c23-4532-b20b-e55419a59985' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-c5f0b87a-f8d8-49e6-98df-8a7933733878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5f0b87a-f8d8-49e6-98df-8a7933733878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7780b12-f29d-487d-adb6-6df5e7f22af1' class='xr-var-data-in' type='checkbox'><label for='data-c7780b12-f29d-487d-adb6-6df5e7f22af1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-12-01 ... 2016-02-01</div><input id='attrs-d8ea9236-a70c-4eb0-8a51-347dda44a2de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8ea9236-a70c-4eb0-8a51-347dda44a2de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-079f4561-f734-4b62-bb2b-f18813b523f6' class='xr-var-data-in' type='checkbox'><label for='data-079f4561-f734-4b62-bb2b-f18813b523f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80384.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-12-01T00:00:00.000000000&#x27;, &#x27;1961-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-02-01T00:00:00.000000000&#x27;, &#x27;1961-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-01-01T00:00:00.000000000&#x27;, &#x27;1962-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-12-01T00:00:00.000000000&#x27;, &#x27;1963-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1963-02-01T00:00:00.000000000&#x27;, &#x27;1963-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-01-01T00:00:00.000000000&#x27;, &#x27;1964-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-12-01T00:00:00.000000000&#x27;, &#x27;1965-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1965-02-01T00:00:00.000000000&#x27;, &#x27;1965-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-01-01T00:00:00.000000000&#x27;, &#x27;1966-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-12-01T00:00:00.000000000&#x27;, &#x27;1967-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1967-02-01T00:00:00.000000000&#x27;, &#x27;1967-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-01-01T00:00:00.000000000&#x27;, &#x27;1968-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-12-01T00:00:00.000000000&#x27;, &#x27;1969-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1969-02-01T00:00:00.000000000&#x27;, &#x27;1969-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-01-01T00:00:00.000000000&#x27;, &#x27;1970-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-12-01T00:00:00.000000000&#x27;, &#x27;1971-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1971-02-01T00:00:00.000000000&#x27;, &#x27;1971-12-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1975-02-01T00:00:00.000000000&#x27;, &#x27;1975-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1976-01-01T00:00:00.000000000&#x27;, &#x27;1976-02-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1977-02-01T00:00:00.000000000&#x27;, &#x27;1977-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-01-01T00:00:00.000000000&#x27;, &#x27;1978-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-12-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-02-01T00:00:00.000000000&#x27;, &#x27;1979-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-01T00:00:00.000000000&#x27;, &#x27;1981-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-02-01T00:00:00.000000000&#x27;, &#x27;1981-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-01T00:00:00.000000000&#x27;, &#x27;1982-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-12-01T00:00:00.000000000&#x27;, &#x27;1983-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-02-01T00:00:00.000000000&#x27;, &#x27;1983-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-01-01T00:00:00.000000000&#x27;, &#x27;1984-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-12-01T00:00:00.000000000&#x27;, &#x27;1985-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-02-01T00:00:00.000000000&#x27;, &#x27;1985-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-01-01T00:00:00.000000000&#x27;, &#x27;1986-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-12-01T00:00:00.000000000&#x27;, &#x27;1987-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-02-01T00:00:00.000000000&#x27;, &#x27;1987-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-01-01T00:00:00.000000000&#x27;, &#x27;1988-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-12-01T00:00:00.000000000&#x27;, &#x27;1989-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-02-01T00:00:00.000000000&#x27;, &#x27;1989-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-01-01T00:00:00.000000000&#x27;, &#x27;1990-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-12-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-02-01T00:00:00.000000000&#x27;, &#x27;1991-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1992-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-12-01T00:00:00.000000000&#x27;, &#x27;1993-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-02-01T00:00:00.000000000&#x27;, &#x27;1993-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-01-01T00:00:00.000000000&#x27;, &#x27;1994-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-12-01T00:00:00.000000000&#x27;, &#x27;1995-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-02-01T00:00:00.000000000&#x27;, &#x27;1995-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-01-01T00:00:00.000000000&#x27;, &#x27;1996-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-12-01T00:00:00.000000000&#x27;, &#x27;1997-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-02-01T00:00:00.000000000&#x27;, &#x27;1997-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1998-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-12-01T00:00:00.000000000&#x27;, &#x27;1999-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-02-01T00:00:00.000000000&#x27;, &#x27;1999-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-01T00:00:00.000000000&#x27;, &#x27;2001-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-02-01T00:00:00.000000000&#x27;, &#x27;2001-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-12-01T00:00:00.000000000&#x27;, &#x27;2003-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-02-01T00:00:00.000000000&#x27;, &#x27;2003-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2004-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-12-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-02-01T00:00:00.000000000&#x27;, &#x27;2005-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2006-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-12-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-02-01T00:00:00.000000000&#x27;, &#x27;2007-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2008-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-12-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-02-01T00:00:00.000000000&#x27;, &#x27;2009-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-02-01T00:00:00.000000000&#x27;, &#x27;2011-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-12-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-02-01T00:00:00.000000000&#x27;, &#x27;2013-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2014-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-02-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ac9a4c5-b62b-4798-a387-a5886151c886' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4ac9a4c5-b62b-4798-a387-a5886151c886' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>actual_range :</span></dt><dd>[-1.8  33.95]</dd><dt><span>valid_range :</span></dt><dd>[-5. 40.]</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V4</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 168, lat: 89, lon: 180)>\n",
       "[2691360 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-12-01 1961-01-01 ... 2016-02-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    actual_range:  [-1.8  33.95]\n",
       "    valid_range:   [-5. 40.]\n",
       "    dataset:       NOAA Extended Reconstructed SST V4\n",
       "    parent_stat:   Individual Values"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst = xr.open_dataset('data\\\\sst.mnmean.v4.nc')['sst']\n",
    "sst = sst.loc[sst.time.dt.month.isin([12, 1, 2])].loc['1960-03-01':'2016-03-01', :, :]\n",
    "sst"
   ]
  },
  {
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   "source": [
    "# 把今年的12月和明年的1、2月当作今年的冬季\n",
    "# 创建一个1961-2010的一维矩阵\n",
    "def winsel(hgt500):\n",
    "    winhgt500 = np.zeros((int(len(hgt500.time) / 3), len(hgt500.lat), len(hgt500.lon)))\n",
    "    temp = np.zeros((len(hgt500.lat), len(hgt500.lon)))\n",
    "    j = 0\n",
    "    for i in range(len(hgt500.time)):\n",
    "        temp += hgt500[i, :, :]\n",
    "        if (i + 1) % 3 == 0:\n",
    "            winhgt500[j, :, :] = temp / 3\n",
    "            j += 1\n",
    "            temp = 0\n",
    "    # 把win转化成 DataArray\n",
    "    winhgt500 = xr.DataArray(data=winhgt500, dims=['time', 'lat', 'lon'],\n",
    "                             coords={'time': pd.date_range('1960', '2016', freq='1y'),\n",
    "                                     'lat': hgt500.lat.data,\n",
    "                                     'lon': hgt500.lon.data})\n",
    "    return winhgt500"
   ]
  },
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   "execution_count": 6,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 56, lat: 89, lon: 180)&gt;\n",
       "array([[[-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "        [-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "        [-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "        [-1.70333326, -1.69666672, -1.68999994, ..., -1.75999987,\n",
       "         -1.73000002, -1.71333325],\n",
       "        [-1.67666662, -1.65666676, -1.63333333, ..., -1.71999991,\n",
       "         -1.69333327, -1.6833334 ],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1961-12-31 ... 2015-12-31\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 56</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8e5e22b7-01fc-4293-aac1-9102ea75be94' class='xr-array-in' type='checkbox' checked><label for='section-8e5e22b7-01fc-4293-aac1-9102ea75be94' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "        [-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "        [-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[-1.79999983, -1.79999983, -1.79999983, ..., -1.79999983,\n",
       "         -1.79999983, -1.79999983],\n",
       "        [-1.70333326, -1.69666672, -1.68999994, ..., -1.75999987,\n",
       "         -1.73000002, -1.71333325],\n",
       "        [-1.67666662, -1.65666676, -1.63333333, ..., -1.71999991,\n",
       "         -1.69333327, -1.6833334 ],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-045ad410-9bb0-4e34-9fb0-6c83ce1c58c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-045ad410-9bb0-4e34-9fb0-6c83ce1c58c1' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-12-31 ... 2015-12-31</div><input id='attrs-ff119cf7-ec8d-4589-a327-b1faaba7145a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff119cf7-ec8d-4589-a327-b1faaba7145a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac2a0f43-d295-4056-8e80-48b8ac1f826c' class='xr-var-data-in' type='checkbox'><label for='data-ac2a0f43-d295-4056-8e80-48b8ac1f826c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-12-31T00:00:00.000000000&#x27;, &#x27;1961-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-12-31T00:00:00.000000000&#x27;, &#x27;1963-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-12-31T00:00:00.000000000&#x27;, &#x27;1965-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-12-31T00:00:00.000000000&#x27;, &#x27;1967-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-12-31T00:00:00.000000000&#x27;, &#x27;1969-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-12-31T00:00:00.000000000&#x27;, &#x27;1971-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1972-12-31T00:00:00.000000000&#x27;, &#x27;1973-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1974-12-31T00:00:00.000000000&#x27;, &#x27;1975-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1976-12-31T00:00:00.000000000&#x27;, &#x27;1977-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-12-31T00:00:00.000000000&#x27;, &#x27;1979-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-31T00:00:00.000000000&#x27;, &#x27;1981-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-12-31T00:00:00.000000000&#x27;, &#x27;1983-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-12-31T00:00:00.000000000&#x27;, &#x27;1985-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-12-31T00:00:00.000000000&#x27;, &#x27;1987-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-12-31T00:00:00.000000000&#x27;, &#x27;1989-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-12-31T00:00:00.000000000&#x27;, &#x27;1991-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-12-31T00:00:00.000000000&#x27;, &#x27;1993-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-12-31T00:00:00.000000000&#x27;, &#x27;1995-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-12-31T00:00:00.000000000&#x27;, &#x27;1997-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-12-31T00:00:00.000000000&#x27;, &#x27;1999-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-31T00:00:00.000000000&#x27;, &#x27;2001-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-12-31T00:00:00.000000000&#x27;, &#x27;2003-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-12-31T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-12-31T00:00:00.000000000&#x27;, &#x27;2007-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-12-31T00:00:00.000000000&#x27;, &#x27;2009-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-31T00:00:00.000000000&#x27;, &#x27;2011-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-12-31T00:00:00.000000000&#x27;, &#x27;2013-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-309170a9-4254-4706-b944-e8c614e89d35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-309170a9-4254-4706-b944-e8c614e89d35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95299199-c85d-41ea-9bd0-4d0797ae9cdf' class='xr-var-data-in' type='checkbox'><label for='data-95299199-c85d-41ea-9bd0-4d0797ae9cdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-51dc5f23-3aad-451d-a55b-3f6e92ed1bfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51dc5f23-3aad-451d-a55b-3f6e92ed1bfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59cd0db9-a3d7-4f4d-91de-0d80f6d1f67f' class='xr-var-data-in' type='checkbox'><label for='data-59cd0db9-a3d7-4f4d-91de-0d80f6d1f67f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b5b31ec-3151-42de-a4ff-789922041b40' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b5b31ec-3151-42de-a4ff-789922041b40' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "                 nan,         nan],\n",
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       "                 nan,         nan],\n",
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       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1961-12-31 ... 2015-12-31\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0"
      ]
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "winhgt500 = winsel(hgt)\n",
    "winsst = winsel(sst)\n",
    "winsst"
   ]
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   "source": [
    "# 计算长江中下游（26-33N/106-122E）1961-2016年夏季降水指数，并画出标准化的长江中下游夏季降水序列图"
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       "       578.63654, 484.3973 , 642.1881 , 532.0575 , 604.6704 , 702.30743,\n",
       "       543.22394, 711.4294 , 693.68646, 583.9099 , 484.97034, 617.08167,\n",
       "       497.77444, 515.78046, 528.83954, 470.35928, 598.56   , 589.0776 ,\n",
       "       516.5423 , 601.75037, 563.15356, 521.76953, 499.26273, 652.2213 ,\n",
       "       682.65027, 627.58014], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1961 1962 1963 1964 1965 ... 2012 2013 2014 2015 2016</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 56</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-10e44c62-c4ff-4aa2-ad53-72f7888fe1d6' class='xr-array-in' type='checkbox' checked><label for='section-10e44c62-c4ff-4aa2-ad53-72f7888fe1d6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>438.8 583.6 483.1 513.0 535.4 399.7 ... 521.8 499.3 652.2 682.7 627.6</span></div><div class='xr-array-data'><pre>array([438.82404, 583.63837, 483.08777, 513.0076 , 535.3992 , 399.72733,\n",
       "       453.15222, 549.24725, 659.74927, 520.82104, 450.0766 , 418.15607,\n",
       "       527.55914, 521.24615, 563.11334, 475.9609 , 581.7569 , 369.30258,\n",
       "       553.33704, 698.9575 , 450.12708, 629.9874 , 631.51184, 531.4543 ,\n",
       "       437.77524, 505.6451 , 579.5008 , 489.67627, 560.52136, 475.50604,\n",
       "       578.63654, 484.3973 , 642.1881 , 532.0575 , 604.6704 , 702.30743,\n",
       "       543.22394, 711.4294 , 693.68646, 583.9099 , 484.97034, 617.08167,\n",
       "       497.77444, 515.78046, 528.83954, 470.35928, 598.56   , 589.0776 ,\n",
       "       516.5423 , 601.75037, 563.15356, 521.76953, 499.26273, 652.2213 ,\n",
       "       682.65027, 627.58014], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-22ffc6fb-e9d5-4fd9-9798-b1eeb6b2face' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22ffc6fb-e9d5-4fd9-9798-b1eeb6b2face' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1961 1962 1963 ... 2014 2015 2016</div><input id='attrs-ef7a9a94-3a2a-446d-867a-add7cf8ae876' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef7a9a94-3a2a-446d-867a-add7cf8ae876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0618b93-cca1-4352-9f16-311d25d61df1' class='xr-var-data-in' type='checkbox'><label for='data-c0618b93-cca1-4352-9f16-311d25d61df1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,\n",
       "       1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984,\n",
       "       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
       "       2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3a94e43-c5e9-4fdd-9ce5-2c558ee21e30' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3a94e43-c5e9-4fdd-9ce5-2c558ee21e30' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.DataArray 'pre' (year: 56)>\n",
       "array([438.82404, 583.63837, 483.08777, 513.0076 , 535.3992 , 399.72733,\n",
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   "source": [
    "rainzhi = rain.mean(dim=['lat', 'lon'])\n",
    "rainzhi"
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       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pre&#x27; (year: 56)&gt;\n",
       "array([-1.3586963 ,  0.46026823, -0.8027143 , -0.4269009 , -0.14564823,\n",
       "       -1.849777  , -1.1787248 ,  0.02829293,  1.4162719 , -0.3287592 ,\n",
       "       -1.2173566 , -1.6182997 , -0.24412419, -0.32341954,  0.20246024,\n",
       "       -0.8922324 ,  0.43663573, -2.2319322 ,  0.07966333,  1.9087536 ,\n",
       "       -1.2167226 ,  1.0424433 ,  1.061591  , -0.19519861, -1.3718699 ,\n",
       "       -0.519379  ,  0.40829757, -0.7199583 ,  0.16990325, -0.8979458 ,\n",
       "        0.3974419 , -0.7862656 ,  1.1956921 , -0.18762189,  0.72444475,\n",
       "        1.9508308 , -0.04736393,  2.0654085 ,  1.8425456 ,  0.46367902,\n",
       "       -0.779068  ,  0.8803384 , -0.6182399 , -0.39207235, -0.22804157,\n",
       "       -0.96259254,  0.6476939 ,  0.52858835, -0.38250312,  0.687767  ,\n",
       "        0.20296547, -0.31684557, -0.5995461 ,  1.3217157 ,  1.7039237 ,\n",
       "        1.0122062 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1961 1962 1963 1964 1965 ... 2012 2013 2014 2015 2016</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 56</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d624fa73-1c2d-4507-86d3-a9ab86d11495' class='xr-array-in' type='checkbox' checked><label for='section-d624fa73-1c2d-4507-86d3-a9ab86d11495' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.359 0.4603 -0.8027 -0.4269 -0.1456 ... -0.5995 1.322 1.704 1.012</span></div><div class='xr-array-data'><pre>array([-1.3586963 ,  0.46026823, -0.8027143 , -0.4269009 , -0.14564823,\n",
       "       -1.849777  , -1.1787248 ,  0.02829293,  1.4162719 , -0.3287592 ,\n",
       "       -1.2173566 , -1.6182997 , -0.24412419, -0.32341954,  0.20246024,\n",
       "       -0.8922324 ,  0.43663573, -2.2319322 ,  0.07966333,  1.9087536 ,\n",
       "       -1.2167226 ,  1.0424433 ,  1.061591  , -0.19519861, -1.3718699 ,\n",
       "       -0.519379  ,  0.40829757, -0.7199583 ,  0.16990325, -0.8979458 ,\n",
       "        0.3974419 , -0.7862656 ,  1.1956921 , -0.18762189,  0.72444475,\n",
       "        1.9508308 , -0.04736393,  2.0654085 ,  1.8425456 ,  0.46367902,\n",
       "       -0.779068  ,  0.8803384 , -0.6182399 , -0.39207235, -0.22804157,\n",
       "       -0.96259254,  0.6476939 ,  0.52858835, -0.38250312,  0.687767  ,\n",
       "        0.20296547, -0.31684557, -0.5995461 ,  1.3217157 ,  1.7039237 ,\n",
       "        1.0122062 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-168f63cc-c10d-4484-a9b2-b2923512bc91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-168f63cc-c10d-4484-a9b2-b2923512bc91' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1961 1962 1963 ... 2014 2015 2016</div><input id='attrs-a60e4461-329d-4d25-9d29-0165ecc9ee99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a60e4461-329d-4d25-9d29-0165ecc9ee99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e41754f1-e666-4a8d-93a6-768222516b8e' class='xr-var-data-in' type='checkbox'><label for='data-e41754f1-e666-4a8d-93a6-768222516b8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,\n",
       "       1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984,\n",
       "       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
       "       2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c29f04ad-69d7-4402-bd38-a919e1ee6474' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c29f04ad-69d7-4402-bd38-a919e1ee6474' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pre' (year: 56)>\n",
       "array([-1.3586963 ,  0.46026823, -0.8027143 , -0.4269009 , -0.14564823,\n",
       "       -1.849777  , -1.1787248 ,  0.02829293,  1.4162719 , -0.3287592 ,\n",
       "       -1.2173566 , -1.6182997 , -0.24412419, -0.32341954,  0.20246024,\n",
       "       -0.8922324 ,  0.43663573, -2.2319322 ,  0.07966333,  1.9087536 ,\n",
       "       -1.2167226 ,  1.0424433 ,  1.061591  , -0.19519861, -1.3718699 ,\n",
       "       -0.519379  ,  0.40829757, -0.7199583 ,  0.16990325, -0.8979458 ,\n",
       "        0.3974419 , -0.7862656 ,  1.1956921 , -0.18762189,  0.72444475,\n",
       "        1.9508308 , -0.04736393,  2.0654085 ,  1.8425456 ,  0.46367902,\n",
       "       -0.779068  ,  0.8803384 , -0.6182399 , -0.39207235, -0.22804157,\n",
       "       -0.96259254,  0.6476939 ,  0.52858835, -0.38250312,  0.687767  ,\n",
       "        0.20296547, -0.31684557, -0.5995461 ,  1.3217157 ,  1.7039237 ,\n",
       "        1.0122062 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1961 1962 1963 1964 1965 ... 2012 2013 2014 2015 2016"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标准化\n",
    "rainzhi = (rainzhi - rainzhi.mean()) / rainzhi.std()\n",
    "rainzhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "fig = plt.figure(figsize=(9, 6))\n",
    "ax = fig.subplots(1, 1)\n",
    "# 标题\n",
    "ax.set_title('标准化的长江中下游的夏季降水序列图')\n",
    "\n",
    "ax.plot(rainzhi.year, rainzhi.data, color=\"black\")\n",
    "ax.set_xticks(np.arange(1961, 2017, 5))\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('data\\\\ex7-1.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 计算长江中下游夏季降水指数与前期冬季（DJF）海温场的相关系数，并画图，选出关键区，并将关键区的区域平均海温指数作为潜在预报因子"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 相关系数公式\n",
    "$r=\\frac{\\frac{1}{n}\\sum_{i=1}^{n}(X_{i}-\\overline{X})(Y_{i}-\\overline{Y})}{\\frac{1}{n}\\sqrt{\\sum_{i=1}^{n}(X_{i}-\\overline{X})^2}\\sqrt{\\sum_{i=1}^{n}(Y_{i}-\\overline{Y})^2}}$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 相关系数t检验公式\n",
    "$|t|=|\\frac{r\\sqrt{n-2}}{\\sqrt{1-r^2}}|>t_{\\alpha }$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 计算夏季降水指数与前期冬季500hPa高度场和海温的相关系数 （空间场）\n",
    "def xiangguan(WP, ds):\n",
    "    aveWP = np.mean(WP)\n",
    "    x = WP - aveWP\n",
    "    y = ds.loc[:, :, :] - ds.loc[:, :, :].mean(dim='time')\n",
    "    x = np.array(x).reshape((len(WP), 1, 1))\n",
    "    up = np.sum(x * y, axis=0) / len(WP.year)\n",
    "    down = (np.sqrt(np.sum(x ** 2)) * np.sqrt(np.sum(y ** 2, axis=0))) / len(WP.year)\n",
    "    # 相关系数\n",
    "    r = up / down\n",
    "    return r\n",
    "\n",
    "# t检验\n",
    "def tjianyan(r,n):\n",
    "    return np.abs(r*np.sqrt(n-2)/np.sqrt(1-np.power(r,2)))"
   ]
  },
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     "name": "stderr",
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     "text": [
      "d:\\anaconda\\envs\\py310\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1879: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 56, lat: 89, lon: 180)&gt;\n",
       "array([[[-7.41619848, -7.41619848, -7.41619848, ..., -7.41619848,\n",
       "         -7.41619848, -7.41619848],\n",
       "        [-0.26911408, -0.29077839, -0.29548019, ..., -0.14581327,\n",
       "         -0.15365623, -0.18330184],\n",
       "        [-0.25492678, -0.27415196, -0.29416064, ..., -0.24247626,\n",
       "         -0.26099787, -0.27142389],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 0.13483998,  0.13483998,  0.13483998, ...,  0.13483998,\n",
       "          0.13483998,  0.13483998],\n",
       "        [-0.26910627, -0.29077219, -0.29547471, ..., -0.14579081,\n",
       "         -0.15364341, -0.18329164],\n",
       "        [-0.25492273, -0.27414835, -0.29415764, ..., -0.24246655,\n",
       "         -0.26099158, -0.27141921],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 0.13483998,  0.13483998,  0.13483998, ...,  0.13483998,\n",
       "          0.13483998,  0.13483998],\n",
       "        [ 6.06465119,  5.07923743,  4.76043254, ...,  7.39051399,\n",
       "          7.37492705,  7.229859  ],\n",
       "        [ 3.93714231,  4.06894222,  3.90809587, ...,  6.27505768,\n",
       "          5.37392544,  4.31463183],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1961-12-31 ... 2015-12-31\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 56</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-33584d6f-f96a-432e-b19b-59526f1cd31d' class='xr-array-in' type='checkbox' checked><label for='section-33584d6f-f96a-432e-b19b-59526f1cd31d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-7.416 -7.416 -7.416 -0.1349 -0.1349 -0.1349 ... nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-7.41619848, -7.41619848, -7.41619848, ..., -7.41619848,\n",
       "         -7.41619848, -7.41619848],\n",
       "        [-0.26911408, -0.29077839, -0.29548019, ..., -0.14581327,\n",
       "         -0.15365623, -0.18330184],\n",
       "        [-0.25492678, -0.27415196, -0.29416064, ..., -0.24247626,\n",
       "         -0.26099787, -0.27142389],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 0.13483998,  0.13483998,  0.13483998, ...,  0.13483998,\n",
       "          0.13483998,  0.13483998],\n",
       "        [-0.26910627, -0.29077219, -0.29547471, ..., -0.14579081,\n",
       "         -0.15364341, -0.18329164],\n",
       "        [-0.25492273, -0.27414835, -0.29415764, ..., -0.24246655,\n",
       "         -0.26099158, -0.27141921],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 0.13483998,  0.13483998,  0.13483998, ...,  0.13483998,\n",
       "          0.13483998,  0.13483998],\n",
       "        [ 6.06465119,  5.07923743,  4.76043254, ...,  7.39051399,\n",
       "          7.37492705,  7.229859  ],\n",
       "        [ 3.93714231,  4.06894222,  3.90809587, ...,  6.27505768,\n",
       "          5.37392544,  4.31463183],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-d3b867fd-bf50-4c95-8cf0-ad06b699fefa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3b867fd-bf50-4c95-8cf0-ad06b699fefa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-12-31 ... 2015-12-31</div><input id='attrs-0912a69a-f843-446c-9e6a-48fa74ac163b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0912a69a-f843-446c-9e6a-48fa74ac163b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d28e6e50-869d-487e-bc26-284366768664' class='xr-var-data-in' type='checkbox'><label for='data-d28e6e50-869d-487e-bc26-284366768664' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-12-31T00:00:00.000000000&#x27;, &#x27;1961-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-12-31T00:00:00.000000000&#x27;, &#x27;1963-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-12-31T00:00:00.000000000&#x27;, &#x27;1965-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-12-31T00:00:00.000000000&#x27;, &#x27;1967-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-12-31T00:00:00.000000000&#x27;, &#x27;1969-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-12-31T00:00:00.000000000&#x27;, &#x27;1971-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1972-12-31T00:00:00.000000000&#x27;, &#x27;1973-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1974-12-31T00:00:00.000000000&#x27;, &#x27;1975-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1976-12-31T00:00:00.000000000&#x27;, &#x27;1977-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-12-31T00:00:00.000000000&#x27;, &#x27;1979-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-31T00:00:00.000000000&#x27;, &#x27;1981-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-12-31T00:00:00.000000000&#x27;, &#x27;1983-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-12-31T00:00:00.000000000&#x27;, &#x27;1985-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-12-31T00:00:00.000000000&#x27;, &#x27;1987-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-12-31T00:00:00.000000000&#x27;, &#x27;1989-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-12-31T00:00:00.000000000&#x27;, &#x27;1991-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-12-31T00:00:00.000000000&#x27;, &#x27;1993-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-12-31T00:00:00.000000000&#x27;, &#x27;1995-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-12-31T00:00:00.000000000&#x27;, &#x27;1997-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-12-31T00:00:00.000000000&#x27;, &#x27;1999-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-31T00:00:00.000000000&#x27;, &#x27;2001-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-12-31T00:00:00.000000000&#x27;, &#x27;2003-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-12-31T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-12-31T00:00:00.000000000&#x27;, &#x27;2007-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-12-31T00:00:00.000000000&#x27;, &#x27;2009-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-31T00:00:00.000000000&#x27;, &#x27;2011-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-12-31T00:00:00.000000000&#x27;, &#x27;2013-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-9861a5ee-a3e6-49c2-a082-d848e4657e2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9861a5ee-a3e6-49c2-a082-d848e4657e2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f73f606-befe-49ba-a53c-391e4be718d6' class='xr-var-data-in' type='checkbox'><label for='data-6f73f606-befe-49ba-a53c-391e4be718d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-dc31d9c1-9c99-4f58-a401-72e27269f136' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc31d9c1-9c99-4f58-a401-72e27269f136' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56e61584-a149-498b-a699-6131ea031987' class='xr-var-data-in' type='checkbox'><label for='data-56e61584-a149-498b-a699-6131ea031987' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1ad4df86-6df9-451e-9301-4dd6091a12a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1ad4df86-6df9-451e-9301-4dd6091a12a9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "                 nan,         nan],\n",
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       "         -0.26099158, -0.27141921],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 0.13483998,  0.13483998,  0.13483998, ...,  0.13483998,\n",
       "          0.13483998,  0.13483998],\n",
       "        [ 6.06465119,  5.07923743,  4.76043254, ...,  7.39051399,\n",
       "          7.37492705,  7.229859  ],\n",
       "        [ 3.93714231,  4.06894222,  3.90809587, ...,  6.27505768,\n",
       "          5.37392544,  4.31463183],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1961-12-31 ... 2015-12-31\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 前期冬季海温的标准化\n",
    "winsstb = (winsst - winsst.mean(dim='time')) / winsst.std(dim='time')\n",
    "winsstb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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    "pycharm": {
     "name": "#%%\n"
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       "  display: none;\n",
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       "\n",
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       "  display: inline-block;\n",
       "}\n",
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (lat: 89, lon: 180)&gt;\n",
       "array([[0.18320658, 0.18320658, 0.18320658, ..., 0.18320658, 0.18320658,\n",
       "        0.18320658],\n",
       "       [0.16748788, 0.22091377, 0.23454994, ..., 0.12949658, 0.12818533,\n",
       "        0.15613782],\n",
       "       [0.30984175, 0.29747315, 0.26751927, ..., 0.2130411 , 0.27193952,\n",
       "        0.29492294],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b86e0387-8403-4a64-a15c-21199c47a92d' class='xr-array-in' type='checkbox' checked><label for='section-b86e0387-8403-4a64-a15c-21199c47a92d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.1832 0.1832 0.1832 0.1782 0.1782 0.1782 ... nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[0.18320658, 0.18320658, 0.18320658, ..., 0.18320658, 0.18320658,\n",
       "        0.18320658],\n",
       "       [0.16748788, 0.22091377, 0.23454994, ..., 0.12949658, 0.12818533,\n",
       "        0.15613782],\n",
       "       [0.30984175, 0.29747315, 0.26751927, ..., 0.2130411 , 0.27193952,\n",
       "        0.29492294],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-f442e9ab-a298-41e3-873c-60830973a21e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f442e9ab-a298-41e3-873c-60830973a21e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-2424947a-b013-466f-8638-7ca96f4e76a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2424947a-b013-466f-8638-7ca96f4e76a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f2cdf4-ef5a-4657-aa79-2d7abcdae247' class='xr-var-data-in' type='checkbox'><label for='data-f9f2cdf4-ef5a-4657-aa79-2d7abcdae247' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-16171ed3-0b64-4c8f-87dd-80b91ec3377f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16171ed3-0b64-4c8f-87dd-80b91ec3377f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cab58ab-c9b9-4483-b3cc-773d196dab9e' class='xr-var-data-in' type='checkbox'><label for='data-2cab58ab-c9b9-4483-b3cc-773d196dab9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee2dd487-2f48-491e-9009-36d4112724b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee2dd487-2f48-491e-9009-36d4112724b2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (lat: 89, lon: 180)>\n",
       "array([[0.18320658, 0.18320658, 0.18320658, ..., 0.18320658, 0.18320658,\n",
       "        0.18320658],\n",
       "       [0.16748788, 0.22091377, 0.23454994, ..., 0.12949658, 0.12818533,\n",
       "        0.15613782],\n",
       "       [0.30984175, 0.29747315, 0.26751927, ..., 0.2130411 , 0.27193952,\n",
       "        0.29492294],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 夏季降水指数与前期冬季海温的相关系数\n",
    "rain_sst_xg = xiangguan(rainzhi, winsstb)\n",
    "rain_sst_xg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [
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       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (lat: 89, lon: 180)&gt;\n",
       "array([[1.36946695, 1.36946695, 1.36946695, ..., 1.36946695, 1.36946695,\n",
       "        1.36946695],\n",
       "       [1.24841449, 1.6645023 , 1.77304371, ..., 0.95968227, 0.94980161,\n",
       "        1.16162094],\n",
       "       [2.39471075, 2.28962311, 2.04021796, ..., 1.60230978, 2.07659713,\n",
       "        2.26811555],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6a7f8c81-09af-4382-98fc-2e6fdaf278d3' class='xr-array-in' type='checkbox' checked><label for='section-6a7f8c81-09af-4382-98fc-2e6fdaf278d3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.369 1.369 1.369 1.331 1.331 1.331 1.331 ... nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[1.36946695, 1.36946695, 1.36946695, ..., 1.36946695, 1.36946695,\n",
       "        1.36946695],\n",
       "       [1.24841449, 1.6645023 , 1.77304371, ..., 0.95968227, 0.94980161,\n",
       "        1.16162094],\n",
       "       [2.39471075, 2.28962311, 2.04021796, ..., 1.60230978, 2.07659713,\n",
       "        2.26811555],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-218cf797-4599-4dab-956a-705b8e361e67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-218cf797-4599-4dab-956a-705b8e361e67' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-ca2b0fce-fe43-4cb1-b563-1d237684acd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca2b0fce-fe43-4cb1-b563-1d237684acd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd115d90-3b3a-437a-90c2-2e250115fe5e' class='xr-var-data-in' type='checkbox'><label for='data-cd115d90-3b3a-437a-90c2-2e250115fe5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-807025c1-bdd9-4796-b711-478cf7d4cdf2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-807025c1-bdd9-4796-b711-478cf7d4cdf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fc4d0c5-00e4-4d61-b32f-16397b25fc57' class='xr-var-data-in' type='checkbox'><label for='data-9fc4d0c5-00e4-4d61-b32f-16397b25fc57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf822c05-492c-4871-8b24-5cad0c1fc2c9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf822c05-492c-4871-8b24-5cad0c1fc2c9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (lat: 89, lon: 180)>\n",
       "array([[1.36946695, 1.36946695, 1.36946695, ..., 1.36946695, 1.36946695,\n",
       "        1.36946695],\n",
       "       [1.24841449, 1.6645023 , 1.77304371, ..., 0.95968227, 0.94980161,\n",
       "        1.16162094],\n",
       "       [2.39471075, 2.28962311, 2.04021796, ..., 1.60230978, 2.07659713,\n",
       "        2.26811555],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# t检验\n",
    "rain_sst_t=tjianyan(rain_sst_xg,56)\n",
    "rain_sst_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 创建地图\n",
    "def createmap(ax1):\n",
    "    # 海岸线\n",
    "    ax1.coastlines('110m')\n",
    "    # 标注坐标轴\n",
    "    ax1.set_xticks(np.arange(-180, 181, 30), crs=ccrs.PlateCarree())\n",
    "    ax1.set_yticks(np.arange(-90, 91, 20), crs=ccrs.PlateCarree())\n",
    "    # 设置大小刻度\n",
    "    minorticks = MultipleLocator(10)\n",
    "    majorticks = MultipleLocator(30)\n",
    "    ax1.xaxis.set_major_locator(majorticks)\n",
    "    ax1.xaxis.set_minor_locator(minorticks)\n",
    "    ax1.yaxis.set_minor_locator(minorticks)\n",
    "    # 经纬度格式，把0经度设置不加E和W\n",
    "    lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
    "    lat_formatter = LatitudeFormatter()\n",
    "    ax1.xaxis.set_major_formatter(lon_formatter)\n",
    "    ax1.yaxis.set_major_formatter(lat_formatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lat = rain_sst_xg.lat.data\n",
    "lon = rain_sst_xg.lon.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9, 6))\n",
    "ax = fig.subplots(1, 1, subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)})\n",
    "createmap(ax)\n",
    "#确定关键区的边框范围\n",
    "lons=330\n",
    "lone=360\n",
    "lats=25\n",
    "late=50\n",
    "x=[lons,lons,lone,lone,lons]\n",
    "y=[lats,late,late,lats,lats]\n",
    "# 绘图\n",
    "colorbar = ax.contourf(lon, lat, rain_sst_xg.data,cmap='bwr',transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar, extendrect='True', orientation='horizontal', pad=0.05, fraction=0.04, shrink=1)\n",
    "ax.contourf(lon, lat, rain_sst_t, levels=[-10, 0.25, 10], hatches=['...',None], zorder=1, colors=\"none\",transform=ccrs.PlateCarree())\n",
    "\n",
    "# 绘制边框\n",
    "ax.plot(x,y,transform=ccrs.PlateCarree(central_longitude=0))\n",
    "# 标题\n",
    "plt.title('夏季降水指数与前期海温的相关系数')\n",
    "# 保存图片\n",
    "plt.savefig('data/ex7_2.png', dpi=500)"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 56, lat: 73, lon: 144)&gt;\n",
       "array([[[-1.29627492, -1.29627492, -1.29627492, ..., -1.29627492,\n",
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       "        [-1.39537194, -1.38307643, -1.37109333, ..., -1.43594219,\n",
       "         -1.42392598, -1.40935219],\n",
       "        ...,\n",
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       "\n",
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       "        [-0.91267473, -0.94343356, -0.97190178, ..., -0.82131954,\n",
       "         -0.85285665, -0.88235829],\n",
       "...\n",
       "        [-0.57163778, -0.5583226 , -0.54528156, ..., -0.62706496,\n",
       "         -0.60732629, -0.58890706],\n",
       "        [-0.53106362, -0.52376971, -0.51711851, ..., -0.55591237,\n",
       "         -0.54672283, -0.53850578],\n",
       "        [-0.59485825, -0.59485825, -0.59485825, ..., -0.59485825,\n",
       "         -0.59485825, -0.59485825]],\n",
       "\n",
       "       [[ 1.51440399,  1.51440399,  1.51440399, ...,  1.51440399,\n",
       "          1.51440399,  1.51440399],\n",
       "        [ 1.36791663,  1.36327888,  1.35846353, ...,  1.38674138,\n",
       "          1.37993105,  1.37397281],\n",
       "        [ 1.15116629,  1.14388144,  1.13993544, ...,  1.1793019 ,\n",
       "          1.167738  ,  1.15840582],\n",
       "        ...,\n",
       "        [-1.03955628, -1.06520807, -1.09019816, ..., -0.96048153,\n",
       "         -0.98846283, -1.01399333],\n",
       "        [-0.87499279, -0.88647805, -0.89879829, ..., -0.83976336,\n",
       "         -0.8526236 , -0.86341993],\n",
       "        [-0.87015801, -0.87015801, -0.87015801, ..., -0.87015801,\n",
       "         -0.87015801, -0.87015801]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1961-12-31 ... 2015-12-31\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 56</li><li><span class='xr-has-index'>lat</span>: 73</li><li><span class='xr-has-index'>lon</span>: 144</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bad5e3e5-3cf4-4b5e-b332-40beddf1bdcf' class='xr-array-in' type='checkbox' checked><label for='section-bad5e3e5-3cf4-4b5e-b332-40beddf1bdcf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.296 -1.296 -1.296 -1.296 -1.296 ... -0.8702 -0.8702 -0.8702 -0.8702</span></div><div class='xr-array-data'><pre>array([[[-1.29627492, -1.29627492, -1.29627492, ..., -1.29627492,\n",
       "         -1.29627492, -1.29627492],\n",
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       "         -1.48883878, -1.48952154],\n",
       "        [-1.39537194, -1.38307643, -1.37109333, ..., -1.43594219,\n",
       "         -1.42392598, -1.40935219],\n",
       "        ...,\n",
       "        [-0.15734153, -0.15186235, -0.1442232 , ..., -0.16384299,\n",
       "         -0.16639543, -0.16303621],\n",
       "        [-0.29376931, -0.29042637, -0.28507009, ..., -0.30746629,\n",
       "         -0.30451887, -0.29850472],\n",
       "        [-0.42603007, -0.42603007, -0.42603007, ..., -0.42603007,\n",
       "         -0.42603007, -0.42603007]],\n",
       "\n",
       "       [[-0.93014288, -0.93014288, -0.93014288, ..., -0.93014288,\n",
       "         -0.93014288, -0.93014288],\n",
       "        [-0.96531548, -0.98110358, -0.99545401, ..., -0.91895529,\n",
       "         -0.93384191, -0.94883798],\n",
       "        [-0.91267473, -0.94343356, -0.97190178, ..., -0.82131954,\n",
       "         -0.85285665, -0.88235829],\n",
       "...\n",
       "        [-0.57163778, -0.5583226 , -0.54528156, ..., -0.62706496,\n",
       "         -0.60732629, -0.58890706],\n",
       "        [-0.53106362, -0.52376971, -0.51711851, ..., -0.55591237,\n",
       "         -0.54672283, -0.53850578],\n",
       "        [-0.59485825, -0.59485825, -0.59485825, ..., -0.59485825,\n",
       "         -0.59485825, -0.59485825]],\n",
       "\n",
       "       [[ 1.51440399,  1.51440399,  1.51440399, ...,  1.51440399,\n",
       "          1.51440399,  1.51440399],\n",
       "        [ 1.36791663,  1.36327888,  1.35846353, ...,  1.38674138,\n",
       "          1.37993105,  1.37397281],\n",
       "        [ 1.15116629,  1.14388144,  1.13993544, ...,  1.1793019 ,\n",
       "          1.167738  ,  1.15840582],\n",
       "        ...,\n",
       "        [-1.03955628, -1.06520807, -1.09019816, ..., -0.96048153,\n",
       "         -0.98846283, -1.01399333],\n",
       "        [-0.87499279, -0.88647805, -0.89879829, ..., -0.83976336,\n",
       "         -0.8526236 , -0.86341993],\n",
       "        [-0.87015801, -0.87015801, -0.87015801, ..., -0.87015801,\n",
       "         -0.87015801, -0.87015801]]])</pre></div></div></li><li class='xr-section-item'><input id='section-094abe64-c652-4794-860c-ad4845fcdeb7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-094abe64-c652-4794-860c-ad4845fcdeb7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-12-31 ... 2015-12-31</div><input id='attrs-0686681a-77e3-45a8-a715-063242b9eefc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0686681a-77e3-45a8-a715-063242b9eefc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd4bd94c-3de2-426b-8abe-7e6e96f6f673' class='xr-var-data-in' type='checkbox'><label for='data-dd4bd94c-3de2-426b-8abe-7e6e96f6f673' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-12-31T00:00:00.000000000&#x27;, &#x27;1961-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-12-31T00:00:00.000000000&#x27;, &#x27;1963-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-12-31T00:00:00.000000000&#x27;, &#x27;1965-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-12-31T00:00:00.000000000&#x27;, &#x27;1967-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-12-31T00:00:00.000000000&#x27;, &#x27;1969-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-12-31T00:00:00.000000000&#x27;, &#x27;1971-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1972-12-31T00:00:00.000000000&#x27;, &#x27;1973-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1974-12-31T00:00:00.000000000&#x27;, &#x27;1975-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1976-12-31T00:00:00.000000000&#x27;, &#x27;1977-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-12-31T00:00:00.000000000&#x27;, &#x27;1979-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-31T00:00:00.000000000&#x27;, &#x27;1981-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-12-31T00:00:00.000000000&#x27;, &#x27;1983-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-12-31T00:00:00.000000000&#x27;, &#x27;1985-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-12-31T00:00:00.000000000&#x27;, &#x27;1987-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-12-31T00:00:00.000000000&#x27;, &#x27;1989-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-12-31T00:00:00.000000000&#x27;, &#x27;1991-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-12-31T00:00:00.000000000&#x27;, &#x27;1993-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-12-31T00:00:00.000000000&#x27;, &#x27;1995-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-12-31T00:00:00.000000000&#x27;, &#x27;1997-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-12-31T00:00:00.000000000&#x27;, &#x27;1999-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-31T00:00:00.000000000&#x27;, &#x27;2001-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-12-31T00:00:00.000000000&#x27;, &#x27;2003-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-12-31T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-12-31T00:00:00.000000000&#x27;, &#x27;2007-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-12-31T00:00:00.000000000&#x27;, &#x27;2009-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-31T00:00:00.000000000&#x27;, &#x27;2011-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-12-31T00:00:00.000000000&#x27;, &#x27;2013-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 87.5 85.0 ... -87.5 -90.0</div><input id='attrs-b95341bf-e721-44a6-8575-e2a6e38e5b81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b95341bf-e721-44a6-8575-e2a6e38e5b81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00bc9478-f368-4eb2-958e-803c60a8c470' class='xr-var-data-in' type='checkbox'><label for='data-00bc9478-f368-4eb2-958e-803c60a8c470' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,\n",
       "        65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,\n",
       "        40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,\n",
       "        15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,\n",
       "       -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,\n",
       "       -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,\n",
       "       -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,\n",
       "       -85. , -87.5, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-df66f840-2544-403a-adef-65e58f009fdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df66f840-2544-403a-adef-65e58f009fdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01752090-032d-4d4e-a523-91eac1d3471a' class='xr-var-data-in' type='checkbox'><label for='data-01752090-032d-4d4e-a523-91eac1d3471a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc255c82-1f23-47d8-be3c-7404d8fc0cdf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cc255c82-1f23-47d8-be3c-7404d8fc0cdf' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "         -1.29627492, -1.29627492],\n",
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       "\n",
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       "         -0.85285665, -0.88235829],\n",
       "...\n",
       "        [-0.57163778, -0.5583226 , -0.54528156, ..., -0.62706496,\n",
       "         -0.60732629, -0.58890706],\n",
       "        [-0.53106362, -0.52376971, -0.51711851, ..., -0.55591237,\n",
       "         -0.54672283, -0.53850578],\n",
       "        [-0.59485825, -0.59485825, -0.59485825, ..., -0.59485825,\n",
       "         -0.59485825, -0.59485825]],\n",
       "\n",
       "       [[ 1.51440399,  1.51440399,  1.51440399, ...,  1.51440399,\n",
       "          1.51440399,  1.51440399],\n",
       "        [ 1.36791663,  1.36327888,  1.35846353, ...,  1.38674138,\n",
       "          1.37993105,  1.37397281],\n",
       "        [ 1.15116629,  1.14388144,  1.13993544, ...,  1.1793019 ,\n",
       "          1.167738  ,  1.15840582],\n",
       "        ...,\n",
       "        [-1.03955628, -1.06520807, -1.09019816, ..., -0.96048153,\n",
       "         -0.98846283, -1.01399333],\n",
       "        [-0.87499279, -0.88647805, -0.89879829, ..., -0.83976336,\n",
       "         -0.8526236 , -0.86341993],\n",
       "        [-0.87015801, -0.87015801, -0.87015801, ..., -0.87015801,\n",
       "         -0.87015801, -0.87015801]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1961-12-31 ... 2015-12-31\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对高度场进行标准化\n",
    "winhgtb = (winhgt500 - winhgt500.mean(dim='time')) / winhgt500.std(dim='time')\n",
    "winhgtb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  margin-bottom: 5px;\n",
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       "\n",
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       "\n",
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       "\n",
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       "  vertical-align: top;\n",
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       "\n",
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       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (lat: 73, lon: 144)&gt;\n",
       "array([[ 0.05978372,  0.05978372,  0.05978372, ...,  0.05978372,\n",
       "         0.05978372,  0.05978372],\n",
       "       [ 0.06990541,  0.0676043 ,  0.06534816, ...,  0.07689391,\n",
       "         0.07458552,  0.07237227],\n",
       "       [ 0.06243685,  0.05873899,  0.05529392, ...,  0.07351926,\n",
       "         0.0696769 ,  0.06604631],\n",
       "       ...,\n",
       "       [-0.18078087, -0.18378078, -0.18690795, ..., -0.17237849,\n",
       "        -0.17512475, -0.17783867],\n",
       "       [-0.14422398, -0.14560053, -0.14708568, ..., -0.13995769,\n",
       "        -0.14133252, -0.14268531],\n",
       "       [-0.14066733, -0.14066733, -0.14066733, ..., -0.14066733,\n",
       "        -0.14066733, -0.14066733]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 73</li><li><span class='xr-has-index'>lon</span>: 144</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6e95c50c-66ee-493f-b1c7-ae9eff4f3ddf' class='xr-array-in' type='checkbox' checked><label for='section-6e95c50c-66ee-493f-b1c7-ae9eff4f3ddf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.05978 0.05978 0.05978 0.05978 ... -0.1407 -0.1407 -0.1407 -0.1407</span></div><div class='xr-array-data'><pre>array([[ 0.05978372,  0.05978372,  0.05978372, ...,  0.05978372,\n",
       "         0.05978372,  0.05978372],\n",
       "       [ 0.06990541,  0.0676043 ,  0.06534816, ...,  0.07689391,\n",
       "         0.07458552,  0.07237227],\n",
       "       [ 0.06243685,  0.05873899,  0.05529392, ...,  0.07351926,\n",
       "         0.0696769 ,  0.06604631],\n",
       "       ...,\n",
       "       [-0.18078087, -0.18378078, -0.18690795, ..., -0.17237849,\n",
       "        -0.17512475, -0.17783867],\n",
       "       [-0.14422398, -0.14560053, -0.14708568, ..., -0.13995769,\n",
       "        -0.14133252, -0.14268531],\n",
       "       [-0.14066733, -0.14066733, -0.14066733, ..., -0.14066733,\n",
       "        -0.14066733, -0.14066733]])</pre></div></div></li><li class='xr-section-item'><input id='section-52e5e779-7b53-4ece-80b0-c5e9be4c216a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52e5e779-7b53-4ece-80b0-c5e9be4c216a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 87.5 85.0 ... -87.5 -90.0</div><input id='attrs-eef04997-694b-4b53-9863-321e00d2e1e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eef04997-694b-4b53-9863-321e00d2e1e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb42155c-02b1-4034-8560-00ba0c09d836' class='xr-var-data-in' type='checkbox'><label for='data-fb42155c-02b1-4034-8560-00ba0c09d836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,\n",
       "        65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,\n",
       "        40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,\n",
       "        15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,\n",
       "       -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,\n",
       "       -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,\n",
       "       -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,\n",
       "       -85. , -87.5, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-484edaac-e4b2-4ebc-8b86-949bbd9e6e3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-484edaac-e4b2-4ebc-8b86-949bbd9e6e3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42e2a621-1656-476c-87c9-c1a86ca1c64d' class='xr-var-data-in' type='checkbox'><label for='data-42e2a621-1656-476c-87c9-c1a86ca1c64d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1b3f349-d06b-4db4-ae6a-5d7fd7354d49' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f1b3f349-d06b-4db4-ae6a-5d7fd7354d49' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (lat: 73, lon: 144)>\n",
       "array([[ 0.05978372,  0.05978372,  0.05978372, ...,  0.05978372,\n",
       "         0.05978372,  0.05978372],\n",
       "       [ 0.06990541,  0.0676043 ,  0.06534816, ...,  0.07689391,\n",
       "         0.07458552,  0.07237227],\n",
       "       [ 0.06243685,  0.05873899,  0.05529392, ...,  0.07351926,\n",
       "         0.0696769 ,  0.06604631],\n",
       "       ...,\n",
       "       [-0.18078087, -0.18378078, -0.18690795, ..., -0.17237849,\n",
       "        -0.17512475, -0.17783867],\n",
       "       [-0.14422398, -0.14560053, -0.14708568, ..., -0.13995769,\n",
       "        -0.14133252, -0.14268531],\n",
       "       [-0.14066733, -0.14066733, -0.14066733, ..., -0.14066733,\n",
       "        -0.14066733, -0.14066733]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 夏季降水指数与前期冬季hgt 500hPa 的相关系数\n",
    "rain_hgt_xg = xiangguan(rainzhi, winhgtb)\n",
    "rain_hgt_xg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (lat: 73, lon: 144)&gt;\n",
       "array([[0.44010603, 0.44010603, 0.44010603, ..., 0.44010603, 0.44010603,\n",
       "        0.44010603],\n",
       "       [0.51495755, 0.49792726, 0.48123759, ..., 0.56673049, 0.54962032,\n",
       "        0.53322368],\n",
       "       [0.45971223, 0.43238824, 0.40694829, ..., 0.54172001, 0.51326601,\n",
       "        0.48640128],\n",
       "       ...,\n",
       "       [1.35071788, 1.37390881, 1.39812589, ..., 1.28596794, 1.30709838,\n",
       "        1.32801098],\n",
       "       [1.07102298, 1.08146569, 1.0927395 , ..., 1.03869815, 1.04910842,\n",
       "        1.05935785],\n",
       "       [1.04407084, 1.04407084, 1.04407084, ..., 1.04407084, 1.04407084,\n",
       "        1.04407084]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 73</li><li><span class='xr-has-index'>lon</span>: 144</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-13d089ce-970c-4358-a9fc-0a551037296b' class='xr-array-in' type='checkbox' checked><label for='section-13d089ce-970c-4358-a9fc-0a551037296b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.4401 0.4401 0.4401 0.4401 0.4401 ... 1.044 1.044 1.044 1.044 1.044</span></div><div class='xr-array-data'><pre>array([[0.44010603, 0.44010603, 0.44010603, ..., 0.44010603, 0.44010603,\n",
       "        0.44010603],\n",
       "       [0.51495755, 0.49792726, 0.48123759, ..., 0.56673049, 0.54962032,\n",
       "        0.53322368],\n",
       "       [0.45971223, 0.43238824, 0.40694829, ..., 0.54172001, 0.51326601,\n",
       "        0.48640128],\n",
       "       ...,\n",
       "       [1.35071788, 1.37390881, 1.39812589, ..., 1.28596794, 1.30709838,\n",
       "        1.32801098],\n",
       "       [1.07102298, 1.08146569, 1.0927395 , ..., 1.03869815, 1.04910842,\n",
       "        1.05935785],\n",
       "       [1.04407084, 1.04407084, 1.04407084, ..., 1.04407084, 1.04407084,\n",
       "        1.04407084]])</pre></div></div></li><li class='xr-section-item'><input id='section-a9df3d75-caa0-4fe8-83ce-9b2332476146' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9df3d75-caa0-4fe8-83ce-9b2332476146' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 87.5 85.0 ... -87.5 -90.0</div><input id='attrs-1c760aec-4407-4f5e-893e-71abf9fb8f9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c760aec-4407-4f5e-893e-71abf9fb8f9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-545b6bc7-8869-44b4-a34f-d8fc81fc6d54' class='xr-var-data-in' type='checkbox'><label for='data-545b6bc7-8869-44b4-a34f-d8fc81fc6d54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,\n",
       "        65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,\n",
       "        40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,\n",
       "        15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,\n",
       "       -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,\n",
       "       -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,\n",
       "       -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,\n",
       "       -85. , -87.5, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-ea845283-b8a0-4220-8037-332e144dba74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea845283-b8a0-4220-8037-332e144dba74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9f805ae-f64c-405f-9bc4-35edae97c2d7' class='xr-var-data-in' type='checkbox'><label for='data-c9f805ae-f64c-405f-9bc4-35edae97c2d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9bd5241c-4454-499f-8791-6b42bedded90' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9bd5241c-4454-499f-8791-6b42bedded90' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (lat: 73, lon: 144)>\n",
       "array([[0.44010603, 0.44010603, 0.44010603, ..., 0.44010603, 0.44010603,\n",
       "        0.44010603],\n",
       "       [0.51495755, 0.49792726, 0.48123759, ..., 0.56673049, 0.54962032,\n",
       "        0.53322368],\n",
       "       [0.45971223, 0.43238824, 0.40694829, ..., 0.54172001, 0.51326601,\n",
       "        0.48640128],\n",
       "       ...,\n",
       "       [1.35071788, 1.37390881, 1.39812589, ..., 1.28596794, 1.30709838,\n",
       "        1.32801098],\n",
       "       [1.07102298, 1.08146569, 1.0927395 , ..., 1.03869815, 1.04910842,\n",
       "        1.05935785],\n",
       "       [1.04407084, 1.04407084, 1.04407084, ..., 1.04407084, 1.04407084,\n",
       "        1.04407084]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# t检验\n",
    "rain_hgt_t=tjianyan(rain_hgt_xg,56)\n",
    "rain_hgt_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lat = rain_hgt_xg.lat.data\n",
    "lon = rain_hgt_xg.lon.data\n",
    "fig = plt.figure(figsize=(9, 6))\n",
    "ax = fig.subplots(1, 1, subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)})\n",
    "createmap(ax)\n",
    "#确定关键区的边框范围\n",
    "lons=200\n",
    "lone=240\n",
    "lats=30\n",
    "late=52\n",
    "x=[lons,lons,lone,lone,lons]\n",
    "y=[lats,late,late,lats,lats]\n",
    "# 绘图\n",
    "colorbar = ax.contourf(lon, lat, rain_hgt_xg.data,cmap='bwr', transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar, extendrect='True', orientation='horizontal', pad=0.05, fraction=0.04, shrink=1)\n",
    "ax.contourf(lon, lat, rain_hgt_t, levels=[-10, 0.25, 10], hatches=['...',None], zorder=1, colors=\"none\",transform=ccrs.PlateCarree())\n",
    "# 绘制边框\n",
    "ax.plot(x,y,transform=ccrs.PlateCarree(central_longitude=0))\n",
    "# 标题\n",
    "plt.title('夏季降水指数与前期冬季hgt500hPa的相关系数')\n",
    "# 保存图片\n",
    "plt.savefig('data/ex7_3.png', dpi=500)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 海温关键区的范围选取 [25°N-50°N，330-360]\n",
    "# hgt关键区的范围选取 [30°N-52°N，200-240]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 海温指数\n",
    "sstzhi=winsstb.loc[:,50:25,330:360].mean(dim=['lat','lon'])\n",
    "# hgt指数\n",
    "hgtzhi=winhgtb.loc[:,52:30,200:240].mean(dim=['lat','lon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pre&#x27; (year: 56)&gt;\n",
       "array([-1.3586963 ,  0.46026823, -0.8027143 , -0.4269009 , -0.14564823,\n",
       "       -1.849777  , -1.1787248 ,  0.02829293,  1.4162719 , -0.3287592 ,\n",
       "       -1.2173566 , -1.6182997 , -0.24412419, -0.32341954,  0.20246024,\n",
       "       -0.8922324 ,  0.43663573, -2.2319322 ,  0.07966333,  1.9087536 ,\n",
       "       -1.2167226 ,  1.0424433 ,  1.061591  , -0.19519861, -1.3718699 ,\n",
       "       -0.519379  ,  0.40829757, -0.7199583 ,  0.16990325, -0.8979458 ,\n",
       "        0.3974419 , -0.7862656 ,  1.1956921 , -0.18762189,  0.72444475,\n",
       "        1.9508308 , -0.04736393,  2.0654085 ,  1.8425456 ,  0.46367902,\n",
       "       -0.779068  ,  0.8803384 , -0.6182399 , -0.39207235, -0.22804157,\n",
       "       -0.96259254,  0.6476939 ,  0.52858835, -0.38250312,  0.687767  ,\n",
       "        0.20296547, -0.31684557, -0.5995461 ,  1.3217157 ,  1.7039237 ,\n",
       "        1.0122062 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1961 1962 1963 1964 1965 ... 2012 2013 2014 2015 2016</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 56</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-86177412-9e76-43e3-817e-d0a1e5118800' class='xr-array-in' type='checkbox' checked><label for='section-86177412-9e76-43e3-817e-d0a1e5118800' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.359 0.4603 -0.8027 -0.4269 -0.1456 ... -0.5995 1.322 1.704 1.012</span></div><div class='xr-array-data'><pre>array([-1.3586963 ,  0.46026823, -0.8027143 , -0.4269009 , -0.14564823,\n",
       "       -1.849777  , -1.1787248 ,  0.02829293,  1.4162719 , -0.3287592 ,\n",
       "       -1.2173566 , -1.6182997 , -0.24412419, -0.32341954,  0.20246024,\n",
       "       -0.8922324 ,  0.43663573, -2.2319322 ,  0.07966333,  1.9087536 ,\n",
       "       -1.2167226 ,  1.0424433 ,  1.061591  , -0.19519861, -1.3718699 ,\n",
       "       -0.519379  ,  0.40829757, -0.7199583 ,  0.16990325, -0.8979458 ,\n",
       "        0.3974419 , -0.7862656 ,  1.1956921 , -0.18762189,  0.72444475,\n",
       "        1.9508308 , -0.04736393,  2.0654085 ,  1.8425456 ,  0.46367902,\n",
       "       -0.779068  ,  0.8803384 , -0.6182399 , -0.39207235, -0.22804157,\n",
       "       -0.96259254,  0.6476939 ,  0.52858835, -0.38250312,  0.687767  ,\n",
       "        0.20296547, -0.31684557, -0.5995461 ,  1.3217157 ,  1.7039237 ,\n",
       "        1.0122062 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-8845c17e-24a8-4cb9-a263-af74a030e14a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8845c17e-24a8-4cb9-a263-af74a030e14a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1961 1962 1963 ... 2014 2015 2016</div><input id='attrs-20839039-837f-4890-ad8c-e95b1e51ec16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20839039-837f-4890-ad8c-e95b1e51ec16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57f5e9a9-62d9-4d09-be66-c00fcdc3dc4b' class='xr-var-data-in' type='checkbox'><label for='data-57f5e9a9-62d9-4d09-be66-c00fcdc3dc4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,\n",
       "       1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984,\n",
       "       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
       "       2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa843347-04a5-4b50-a934-890e343dc6f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa843347-04a5-4b50-a934-890e343dc6f8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.DataArray 'pre' (year: 56)>\n",
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       "       -1.849777  , -1.1787248 ,  0.02829293,  1.4162719 , -0.3287592 ,\n",
       "       -1.2173566 , -1.6182997 , -0.24412419, -0.32341954,  0.20246024,\n",
       "       -0.8922324 ,  0.43663573, -2.2319322 ,  0.07966333,  1.9087536 ,\n",
       "       -1.2167226 ,  1.0424433 ,  1.061591  , -0.19519861, -1.3718699 ,\n",
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       "        1.9508308 , -0.04736393,  2.0654085 ,  1.8425456 ,  0.46367902,\n",
       "       -0.779068  ,  0.8803384 , -0.6182399 , -0.39207235, -0.22804157,\n",
       "       -0.96259254,  0.6476939 ,  0.52858835, -0.38250312,  0.687767  ,\n",
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       "        1.0122062 ], dtype=float32)\n",
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     "execution_count": 22,
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   "source": [
    "rainzhi"
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    "X=np.zeros((len(rainzhi),3))\n",
    "Y=rainzhi.data\n",
    "X[:,0]=1\n",
    "X[:,1]=sstzhi\n",
    "X[:,2]=hgtzhi"
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     "data": {
      "text/plain": [
       "array([-1.3586963 ,  0.46026823, -0.8027143 , -0.4269009 , -0.14564823,\n",
       "       -1.849777  , -1.1787248 ,  0.02829293,  1.4162719 , -0.3287592 ,\n",
       "       -1.2173566 , -1.6182997 , -0.24412419, -0.32341954,  0.20246024,\n",
       "       -0.8922324 ,  0.43663573, -2.2319322 ,  0.07966333,  1.9087536 ,\n",
       "       -1.2167226 ,  1.0424433 ,  1.061591  , -0.19519861, -1.3718699 ,\n",
       "       -0.519379  ,  0.40829757, -0.7199583 ,  0.16990325, -0.8979458 ,\n",
       "        0.3974419 , -0.7862656 ,  1.1956921 , -0.18762189,  0.72444475,\n",
       "        1.9508308 , -0.04736393,  2.0654085 ,  1.8425456 ,  0.46367902,\n",
       "       -0.779068  ,  0.8803384 , -0.6182399 , -0.39207235, -0.22804157,\n",
       "       -0.96259254,  0.6476939 ,  0.52858835, -0.38250312,  0.687767  ,\n",
       "        0.20296547, -0.31684557, -0.5995461 ,  1.3217157 ,  1.7039237 ,\n",
       "        1.0122062 ], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 多元回归 回归系数 b 计算公式\n",
    "$b=(X^TX)^{-1}X^Ty$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def duob(X,Y):\n",
    "    temp=np.linalg.inv(np.dot(X.T,X))\n",
    "    temp=np.dot(temp,X.T)\n",
    "    b=np.dot(temp,Y)\n",
    "    return b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.25082233e-08,  5.93186289e-01, -2.94609137e-01])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=duob(X,Y)\n",
    "b"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 多元回归方程\n",
    "y=b[0]+b[1]sstzhi+b[2]hgtzhi"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 交叉检验\n",
    "![CV](data/MLCV.jpg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 对于模型  y=b0+b1x1+b2x2 剔除一年当作测试集，其他年份的数据用于计算回归系数 ，测试集用于检测回归系数计算完之后的模型的回归效果\n",
    "# 用于存放真实y 和回归 y\n",
    "E=[]\n",
    "# 用于选取数据\n",
    "for i in range(len(rainzhi)):\n",
    "    bo=np.bool_(np.zeros(len(rainzhi))+1)\n",
    "    bo[i]=False\n",
    "    X1=X[bo,:]\n",
    "    Y1=Y[bo]\n",
    "    b1=duob(X1,Y1)\n",
    "    # 建立模型 y=b[0]+b[1]sstzhi+b[2]hgtzhi 并把测试数据放进去得到回归y\n",
    "    E.append(Y[i]-(b1[0]*X[i,0]+b1[1]*X[i,1]+b1[2]*X[i,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "aveE=np.mean(E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003966787660967823"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aveE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(9,6))\n",
    "ax=fig.subplots(1,1)\n",
    "# 标题\n",
    "ax.set_title('交叉检验E（y-y^）')\n",
    "ax.set_title('aveE= '+ str(round(aveE,5)),loc='right')\n",
    "# 绘图\n",
    "ax.axhline(y=0,color='k')\n",
    "ax.plot(rainzhi.year,E,marker='o')\n",
    "# 刻度\n",
    "ax.set_xticks(np.arange(1961, 2017, 5))\n",
    "ax.set_ylabel('夏季降水指数标准化')\n",
    "# 保存图片\n",
    "plt.savefig('data/ex7_4.png',dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 独立试报(2000-2016)\n",
    "# 个人理解 先拿1961-1999年计算回归系数，回归2000年，然后拿1961-2000计算回归系数，回归2001年\n",
    "# 存放独立试报的值\n",
    "Ypre=[]\n",
    "for i in range(39,56,1):\n",
    "    b2=duob(X[0:i,:],Y[0:i])\n",
    "    Ypre.append(b2[0]+b2[1]*X[i,1]+b2[2]*X[i,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(9,6))\n",
    "ax=fig.subplots(1,1)\n",
    "# 标题\n",
    "ax.set_title('独立试报')\n",
    "\n",
    "ax.plot(np.arange(2000,2017,1),Ypre,color='red',marker='o',label='预测值')\n",
    "ax.plot(np.arange(2000,2017,1),Y[39:],color='green',marker='^',label='观测值')\n",
    "ax.legend()\n",
    "\n",
    "ax1=ax.twinx()\n",
    "ax1.bar(np.arange(2000,2017,1),Y[39:]-Ypre,label='观测值-预测值',alpha=0.5)\n",
    "ax1.axhline(y=0,color='black')\n",
    "\n",
    "ax.set_ylabel('夏季降水指数标准化')\n",
    "ax1.set_ylabel('夏季降水指数标准化差值')\n",
    "ax1.legend(loc='lower right')\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('data/ex7_5.png',dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "1af7d1ae3c55d82bbda2a5569c7a9f2f2d33ff4c43dd1c666120ac4867346c8c"
  },
  "kernelspec": {
   "display_name": "Python 3.10.4 ('py310')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
